Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations468
Missing cells51
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory446.7 KiB
Average record size in memory977.4 B

Variable types

Text5
Categorical7
DateTime1
Numeric5

Alerts

month is highly overall correlated with quarter and 1 other fieldsHigh correlation
quarter is highly overall correlated with month and 2 other fieldsHigh correlation
network is highly overall correlated with date and 8 other fieldsHigh correlation
date is highly overall correlated with network and 5 other fieldsHigh correlation
genre is highly overall correlated with network and 2 other fieldsHigh correlation
subgenre is highly overall correlated with network and 4 other fieldsHigh correlation
episode_count is highly overall correlated with statusHigh correlation
source_type is highly overall correlated with network and 1 other fieldsHigh correlation
status is highly overall correlated with episode_countHigh correlation
order_type is highly overall correlated with unique_rolesHigh correlation
year is highly overall correlated with quarterHigh correlation
season is highly overall correlated with month and 1 other fieldsHigh correlation
unique_roles is highly overall correlated with network and 1 other fieldsHigh correlation
genre is highly imbalanced (61.8%) Imbalance
status is highly imbalanced (97.8%) Imbalance
order_type is highly imbalanced (69.0%) Imbalance
quarter is highly imbalanced (77.7%) Imbalance
season is highly imbalanced (76.0%) Imbalance
team_size has 23 (4.9%) missing values Missing
unique_roles has 23 (4.9%) missing values Missing
show_name has unique values Unique
episode_count has 397 (84.8%) zeros Zeros

Reproduction

Analysis started2025-03-27 03:16:34.118692
Analysis finished2025-03-27 03:16:45.265139
Duration11.15 seconds
Software versionydata-profiling vv4.16.0
Download configurationconfig.json

Variables

show_name
Text

Unique 

Distinct468
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
2025-03-26T20:16:45.483220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length68
Median length37.5
Mean length15.153846
Min length3

Characters and Unicode

Total characters7092
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique468 ?
Unique (%)100.0%

Sample

1st row6666
2nd row3 Body Problem
3rd row61st Street
4th row9-1-1: Nashville
5th rowA Knight of the Seven Kingdoms: The Hedge Knight
ValueCountFrequency (%)
the 140
 
11.3%
of 40
 
3.2%
untitled 28
 
2.3%
project 14
 
1.1%
in 13
 
1.1%
a 12
 
1.0%
11
 
0.9%
dead 9
 
0.7%
and 8
 
0.6%
man 7
 
0.6%
Other values (775) 955
77.2%
2025-03-26T20:16:45.953403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
769
 
10.8%
e 764
 
10.8%
a 427
 
6.0%
o 398
 
5.6%
t 392
 
5.5%
i 390
 
5.5%
n 389
 
5.5%
r 378
 
5.3%
s 279
 
3.9%
l 274
 
3.9%
Other values (62) 2632
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
769
 
10.8%
e 764
 
10.8%
a 427
 
6.0%
o 398
 
5.6%
t 392
 
5.5%
i 390
 
5.5%
n 389
 
5.5%
r 378
 
5.3%
s 279
 
3.9%
l 274
 
3.9%
Other values (62) 2632
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
769
 
10.8%
e 764
 
10.8%
a 427
 
6.0%
o 398
 
5.6%
t 392
 
5.5%
i 390
 
5.5%
n 389
 
5.5%
r 378
 
5.3%
s 279
 
3.9%
l 274
 
3.9%
Other values (62) 2632
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
769
 
10.8%
e 764
 
10.8%
a 427
 
6.0%
o 398
 
5.6%
t 392
 
5.5%
i 390
 
5.5%
n 389
 
5.5%
r 378
 
5.3%
s 279
 
3.9%
l 274
 
3.9%
Other values (62) 2632
37.1%
Distinct443
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
2025-03-26T20:16:46.226130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length343
Median length161
Mean length90.655983
Min length0

Characters and Unicode

Total characters42427
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique440 ?
Unique (%)94.0%

Sample

1st rowTaylor Sheridan (ep), John Linson (ep), Art Linson (ep), David Glasser (ep), Ron Burkle (ep), Bob Yari (ep)
2nd rowAlexander Woo, David Benioff, D.B. Weiss, Derek Tsang, Rian Johnson, Ram Bergman, Brad Pitt, Jeremy Kleiner, Dede Gardner, Rosamund Pike, Robie Uniacke, Lin Qi, Bernadette Caulfield, Nena Rodrigue
3rd rowPeter Moffat, J. David Shanks, Michael B. Jordan, Alana Mayo, Hilary Salmon
4th rowRashad Raisani, Ryan Murphy, Tim Minear
5th rowGeorge R.R. Martin, Ira Parker, Ryan Condal, Vince Gerardis
ValueCountFrequency (%)
ep 726
 
11.4%
w 219
 
3.4%
d 98
 
1.5%
sr 75
 
1.2%
david 67
 
1.1%
executive 47
 
0.7%
producer 47
 
0.7%
michael 41
 
0.6%
john 40
 
0.6%
scott 35
 
0.6%
Other values (2618) 4961
78.1%
2025-03-26T20:16:46.690161image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5912
 
13.9%
e 3907
 
9.2%
a 2995
 
7.1%
, 2351
 
5.5%
n 2325
 
5.5%
r 2275
 
5.4%
i 2092
 
4.9%
o 1703
 
4.0%
l 1498
 
3.5%
t 1217
 
2.9%
Other values (62) 16152
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42427
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5912
 
13.9%
e 3907
 
9.2%
a 2995
 
7.1%
, 2351
 
5.5%
n 2325
 
5.5%
r 2275
 
5.4%
i 2092
 
4.9%
o 1703
 
4.0%
l 1498
 
3.5%
t 1217
 
2.9%
Other values (62) 16152
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42427
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5912
 
13.9%
e 3907
 
9.2%
a 2995
 
7.1%
, 2351
 
5.5%
n 2325
 
5.5%
r 2275
 
5.4%
i 2092
 
4.9%
o 1703
 
4.0%
l 1498
 
3.5%
t 1217
 
2.9%
Other values (62) 16152
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42427
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5912
 
13.9%
e 3907
 
9.2%
a 2995
 
7.1%
, 2351
 
5.5%
n 2325
 
5.5%
r 2275
 
5.4%
i 2092
 
4.9%
o 1703
 
4.0%
l 1498
 
3.5%
t 1217
 
2.9%
Other values (62) 16152
38.1%

network
Categorical

High correlation 

Distinct18
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size29.1 KiB
HBO
88 
Netflix
85 
Prime Video
63 
Apple TV+
53 
Paramount+
49 
Other values (13)
130 

Length

Max length11
Median length10
Mean length6.465812
Min length2

Characters and Unicode

Total characters3026
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)1.3%

Sample

1st rowParamount+
2nd rowNetflix
3rd rowAMC
4th rowABC
5th rowHBO

Common Values

ValueCountFrequency (%)
HBO 88
18.8%
Netflix 85
18.2%
Prime Video 63
13.5%
Apple TV+ 53
11.3%
Paramount+ 49
10.5%
Hulu 47
10.0%
AMC 21
 
4.5%
ABC 16
 
3.4%
Peacock 15
 
3.2%
CBS 13
 
2.8%
Other values (8) 18
 
3.8%

Length

2025-03-26T20:16:46.939766image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hbo 88
15.1%
netflix 85
14.6%
prime 63
10.8%
video 63
10.8%
apple 53
9.1%
tv 53
9.1%
paramount 49
8.4%
hulu 47
8.0%
amc 21
 
3.6%
abc 16
 
2.7%
Other values (10) 46
7.9%

Most occurring characters

ValueCountFrequency (%)
e 281
 
9.3%
i 213
 
7.0%
l 192
 
6.3%
u 143
 
4.7%
H 136
 
4.5%
t 134
 
4.4%
o 131
 
4.3%
P 127
 
4.2%
B 119
 
3.9%
117
 
3.9%
Other values (24) 1433
47.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3026
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 281
 
9.3%
i 213
 
7.0%
l 192
 
6.3%
u 143
 
4.7%
H 136
 
4.5%
t 134
 
4.4%
o 131
 
4.3%
P 127
 
4.2%
B 119
 
3.9%
117
 
3.9%
Other values (24) 1433
47.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3026
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 281
 
9.3%
i 213
 
7.0%
l 192
 
6.3%
u 143
 
4.7%
H 136
 
4.5%
t 134
 
4.4%
o 131
 
4.3%
P 127
 
4.2%
B 119
 
3.9%
117
 
3.9%
Other values (24) 1433
47.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3026
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 281
 
9.3%
i 213
 
7.0%
l 192
 
6.3%
u 143
 
4.7%
H 136
 
4.5%
t 134
 
4.4%
o 131
 
4.3%
P 127
 
4.2%
B 119
 
3.9%
117
 
3.9%
Other values (24) 1433
47.4%

studio
Text

Distinct161
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Memory size34.1 KiB
2025-03-26T20:16:47.173340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length74
Median length65
Mean length16.869658
Min length0

Characters and Unicode

Total characters7895
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique112 ?
Unique (%)23.9%

Sample

1st row101 Studios, MTV Entertainment Studios
2nd rowPlan B
3rd rowAMC Studios
4th row
5th row
ValueCountFrequency (%)
television 151
 
14.7%
studios 101
 
9.8%
productions 39
 
3.8%
20th 38
 
3.7%
warner 33
 
3.2%
bros 33
 
3.2%
amazon 32
 
3.1%
netflix 27
 
2.6%
pictures 25
 
2.4%
entertainment 24
 
2.3%
Other values (225) 525
51.1%
2025-03-26T20:16:47.629672image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 738
 
9.3%
i 716
 
9.1%
622
 
7.9%
o 589
 
7.5%
n 577
 
7.3%
t 478
 
6.1%
s 472
 
6.0%
r 371
 
4.7%
l 317
 
4.0%
a 310
 
3.9%
Other values (56) 2705
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 738
 
9.3%
i 716
 
9.1%
622
 
7.9%
o 589
 
7.5%
n 577
 
7.3%
t 478
 
6.1%
s 472
 
6.0%
r 371
 
4.7%
l 317
 
4.0%
a 310
 
3.9%
Other values (56) 2705
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 738
 
9.3%
i 716
 
9.1%
622
 
7.9%
o 589
 
7.5%
n 577
 
7.3%
t 478
 
6.1%
s 472
 
6.0%
r 371
 
4.7%
l 317
 
4.0%
a 310
 
3.9%
Other values (56) 2705
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 738
 
9.3%
i 716
 
9.1%
622
 
7.9%
o 589
 
7.5%
n 577
 
7.3%
t 478
 
6.1%
s 472
 
6.0%
r 371
 
4.7%
l 317
 
4.0%
a 310
 
3.9%
Other values (56) 2705
34.3%

date
Date

High correlation 

Distinct51
Distinct (%)10.9%
Missing1
Missing (%)0.2%
Memory size3.8 KiB
Minimum2017-11-28 00:00:00
Maximum2025-02-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-26T20:16:47.769988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:47.922892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

genre
Categorical

High correlation  Imbalance 

Distinct8
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size28.6 KiB
Drama
334 
Comedy
112 
Animation
 
11
Action
 
4
Fantasy
 
3
Other values (3)
 
4

Length

Max length9
Median length5
Mean length5.3653846
Min length5

Characters and Unicode

Total characters2511
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowDrama
2nd rowSci-Fi
3rd rowDrama
4th rowDrama
5th rowDrama

Common Values

ValueCountFrequency (%)
Drama 334
71.4%
Comedy 112
 
23.9%
Animation 11
 
2.4%
Action 4
 
0.9%
Fantasy 3
 
0.6%
Sci-Fi 2
 
0.4%
Crime 1
 
0.2%
Thriller 1
 
0.2%

Length

2025-03-26T20:16:48.081816image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T20:16:48.238696image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
drama 334
71.4%
comedy 112
 
23.9%
animation 11
 
2.4%
action 4
 
0.9%
fantasy 3
 
0.6%
sci-fi 2
 
0.4%
crime 1
 
0.2%
thriller 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 685
27.3%
m 458
18.2%
r 337
13.4%
D 334
13.3%
o 127
 
5.1%
y 115
 
4.6%
e 114
 
4.5%
C 113
 
4.5%
d 112
 
4.5%
i 32
 
1.3%
Other values (11) 84
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2511
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 685
27.3%
m 458
18.2%
r 337
13.4%
D 334
13.3%
o 127
 
5.1%
y 115
 
4.6%
e 114
 
4.5%
C 113
 
4.5%
d 112
 
4.5%
i 32
 
1.3%
Other values (11) 84
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2511
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 685
27.3%
m 458
18.2%
r 337
13.4%
D 334
13.3%
o 127
 
5.1%
y 115
 
4.6%
e 114
 
4.5%
C 113
 
4.5%
d 112
 
4.5%
i 32
 
1.3%
Other values (11) 84
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2511
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 685
27.3%
m 458
18.2%
r 337
13.4%
D 334
13.3%
o 127
 
5.1%
y 115
 
4.6%
e 114
 
4.5%
C 113
 
4.5%
d 112
 
4.5%
i 32
 
1.3%
Other values (11) 84
 
3.3%

subgenre
Text

High correlation 

Distinct56
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
2025-03-26T20:16:48.402527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length39
Median length0
Mean length4.008547
Min length0

Characters and Unicode

Total characters1876
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)7.5%

Sample

1st row
2nd rowProcedural
3rd rowTrue Crime, Legal
4th rowSuperhero, Procedural
5th rowYoung Adult
ValueCountFrequency (%)
thriller 31
12.3%
true 25
9.9%
crime 25
9.9%
fiction 20
 
7.9%
science 20
 
7.9%
superhero 19
 
7.5%
young 16
 
6.3%
adult 16
 
6.3%
historical 13
 
5.1%
mystery 11
 
4.3%
Other values (23) 57
22.5%
2025-03-26T20:16:48.736440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 210
 
11.2%
e 201
 
10.7%
i 159
 
8.5%
l 122
 
6.5%
116
 
6.2%
o 111
 
5.9%
c 93
 
5.0%
u 88
 
4.7%
t 79
 
4.2%
n 72
 
3.8%
Other values (28) 625
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1876
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 210
 
11.2%
e 201
 
10.7%
i 159
 
8.5%
l 122
 
6.5%
116
 
6.2%
o 111
 
5.9%
c 93
 
5.0%
u 88
 
4.7%
t 79
 
4.2%
n 72
 
3.8%
Other values (28) 625
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1876
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 210
 
11.2%
e 201
 
10.7%
i 159
 
8.5%
l 122
 
6.5%
116
 
6.2%
o 111
 
5.9%
c 93
 
5.0%
u 88
 
4.7%
t 79
 
4.2%
n 72
 
3.8%
Other values (28) 625
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1876
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 210
 
11.2%
e 201
 
10.7%
i 159
 
8.5%
l 122
 
6.5%
116
 
6.2%
o 111
 
5.9%
c 93
 
5.0%
u 88
 
4.7%
t 79
 
4.2%
n 72
 
3.8%
Other values (28) 625
33.3%

episode_count
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2307692
Minimum0
Maximum12
Zeros397
Zeros (%)84.8%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-03-26T20:16:48.847515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.004307
Coefficient of variation (CV)2.4409995
Kurtosis3.0681011
Mean1.2307692
Median Absolute Deviation (MAD)0
Skewness2.1730142
Sum576
Variance9.0258606
MonotonicityNot monotonic
2025-03-26T20:16:48.965335image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 397
84.8%
10 25
 
5.3%
8 22
 
4.7%
6 17
 
3.6%
4 2
 
0.4%
7 2
 
0.4%
3 1
 
0.2%
12 1
 
0.2%
11 1
 
0.2%
ValueCountFrequency (%)
0 397
84.8%
3 1
 
0.2%
4 2
 
0.4%
6 17
 
3.6%
7 2
 
0.4%
8 22
 
4.7%
10 25
 
5.3%
11 1
 
0.2%
12 1
 
0.2%
ValueCountFrequency (%)
12 1
 
0.2%
11 1
 
0.2%
10 25
 
5.3%
8 22
 
4.7%
7 2
 
0.4%
6 17
 
3.6%
4 2
 
0.4%
3 1
 
0.2%
0 397
84.8%

source_type
Categorical

High correlation 

Distinct10
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
Original
248 
Book
108 
TV Show
38 
Comic
31 
Film
 
19
Other values (5)
 
24

Length

Max length10
Median length8
Mean length6.5641026
Min length4

Characters and Unicode

Total characters3072
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOriginal
2nd rowBook
3rd rowOriginal
4th rowTV Show
5th rowBook

Common Values

ValueCountFrequency (%)
Original 248
53.0%
Book 108
23.1%
TV Show 38
 
8.1%
Comic 31
 
6.6%
Film 19
 
4.1%
Game 8
 
1.7%
True Story 7
 
1.5%
Article 4
 
0.9%
Other 3
 
0.6%
Podcast 2
 
0.4%

Length

2025-03-26T20:16:49.098435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T20:16:49.240439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
original 248
48.3%
book 108
21.1%
tv 38
 
7.4%
show 38
 
7.4%
comic 31
 
6.0%
film 19
 
3.7%
game 8
 
1.6%
true 7
 
1.4%
story 7
 
1.4%
article 4
 
0.8%
Other values (2) 5
 
1.0%

Most occurring characters

ValueCountFrequency (%)
i 550
17.9%
o 294
9.6%
l 271
8.8%
r 269
8.8%
a 258
8.4%
O 251
8.2%
g 248
8.1%
n 248
8.1%
B 108
 
3.5%
k 108
 
3.5%
Other values (19) 467
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 550
17.9%
o 294
9.6%
l 271
8.8%
r 269
8.8%
a 258
8.4%
O 251
8.2%
g 248
8.1%
n 248
8.1%
B 108
 
3.5%
k 108
 
3.5%
Other values (19) 467
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 550
17.9%
o 294
9.6%
l 271
8.8%
r 269
8.8%
a 258
8.4%
O 251
8.2%
g 248
8.1%
n 248
8.1%
B 108
 
3.5%
k 108
 
3.5%
Other values (19) 467
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 550
17.9%
o 294
9.6%
l 271
8.8%
r 269
8.8%
a 258
8.4%
O 251
8.2%
g 248
8.1%
n 248
8.1%
B 108
 
3.5%
k 108
 
3.5%
Other values (19) 467
15.2%

status
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
467 
Cancelled
 
1

Length

Max length9
Median length0
Mean length0.019230769
Min length0

Characters and Unicode

Total characters9
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
467
99.8%
Cancelled 1
 
0.2%

Length

2025-03-26T20:16:49.384024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T20:16:49.473898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
cancelled 1
100.0%

Most occurring characters

ValueCountFrequency (%)
e 2
22.2%
l 2
22.2%
C 1
11.1%
n 1
11.1%
a 1
11.1%
c 1
11.1%
d 1
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2
22.2%
l 2
22.2%
C 1
11.1%
n 1
11.1%
a 1
11.1%
c 1
11.1%
d 1
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2
22.2%
l 2
22.2%
C 1
11.1%
n 1
11.1%
a 1
11.1%
c 1
11.1%
d 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2
22.2%
l 2
22.2%
C 1
11.1%
n 1
11.1%
a 1
11.1%
c 1
11.1%
d 1
11.1%

order_type
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size26.4 KiB
442 
Limited
 
26

Length

Max length7
Median length0
Mean length0.38888889
Min length0

Characters and Unicode

Total characters182
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
442
94.4%
Limited 26
 
5.6%

Length

2025-03-26T20:16:49.586030image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T20:16:49.697807image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
limited 26
100.0%

Most occurring characters

ValueCountFrequency (%)
i 52
28.6%
L 26
14.3%
m 26
14.3%
t 26
14.3%
e 26
14.3%
d 26
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 52
28.6%
L 26
14.3%
m 26
14.3%
t 26
14.3%
e 26
14.3%
d 26
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 52
28.6%
L 26
14.3%
m 26
14.3%
t 26
14.3%
e 26
14.3%
d 26
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 52
28.6%
L 26
14.3%
m 26
14.3%
t 26
14.3%
e 26
14.3%
d 26
14.3%

notes
Text

Distinct124
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size86.4 KiB
2025-03-26T20:16:49.907254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length580
Median length0
Mean length64.277778
Min length0

Characters and Unicode

Total characters30082
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique123 ?
Unique (%)26.3%

Sample

1st rowSet when Comanches still ruled West Texas. No ranch in America is more steeped in the history of the West than the 6666. Encompassing an entire county, it is where the rule of law and the laws of nature merge in a place where the most dangerous thing one does is the next thing. The 6666 is synonymous with the merciless endeavor to raise the finest horses and livestock in the world and ultimately where world-class cowboys are born and made.
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
the 301
 
6.0%
and 169
 
3.3%
a 166
 
3.3%
of 165
 
3.3%
to 126
 
2.5%
in 93
 
1.8%
on 72
 
1.4%
her 60
 
1.2%
is 60
 
1.2%
with 49
 
1.0%
Other values (1943) 3788
75.0%
2025-03-26T20:16:50.334348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4926
16.4%
e 2942
 
9.8%
t 1978
 
6.6%
a 1959
 
6.5%
o 1805
 
6.0%
i 1707
 
5.7%
n 1705
 
5.7%
s 1607
 
5.3%
r 1537
 
5.1%
h 1192
 
4.0%
Other values (75) 8724
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30082
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4926
16.4%
e 2942
 
9.8%
t 1978
 
6.6%
a 1959
 
6.5%
o 1805
 
6.0%
i 1707
 
5.7%
n 1705
 
5.7%
s 1607
 
5.3%
r 1537
 
5.1%
h 1192
 
4.0%
Other values (75) 8724
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30082
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4926
16.4%
e 2942
 
9.8%
t 1978
 
6.6%
a 1959
 
6.5%
o 1805
 
6.0%
i 1707
 
5.7%
n 1705
 
5.7%
s 1607
 
5.3%
r 1537
 
5.1%
h 1192
 
4.0%
Other values (75) 8724
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30082
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4926
16.4%
e 2942
 
9.8%
t 1978
 
6.6%
a 1959
 
6.5%
o 1805
 
6.0%
i 1707
 
5.7%
n 1705
 
5.7%
s 1607
 
5.3%
r 1537
 
5.1%
h 1192
 
4.0%
Other values (75) 8724
29.0%

year
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.9%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2022.0728
Minimum2017
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-03-26T20:16:50.442779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2021
Q12021
median2023
Q32023
95-th percentile2024
Maximum2025
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2128821
Coefficient of variation (CV)0.00059982116
Kurtosis0.58443697
Mean2022.0728
Median Absolute Deviation (MAD)1
Skewness-0.37191629
Sum944308
Variance1.4710829
MonotonicityNot monotonic
2025-03-26T20:16:50.675461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2023 208
44.4%
2021 198
42.3%
2024 21
 
4.5%
2022 20
 
4.3%
2020 7
 
1.5%
2025 7
 
1.5%
2018 3
 
0.6%
2017 2
 
0.4%
2019 1
 
0.2%
(Missing) 1
 
0.2%
ValueCountFrequency (%)
2017 2
 
0.4%
2018 3
 
0.6%
2019 1
 
0.2%
2020 7
 
1.5%
2021 198
42.3%
2022 20
 
4.3%
2023 208
44.4%
2024 21
 
4.5%
2025 7
 
1.5%
ValueCountFrequency (%)
2025 7
 
1.5%
2024 21
 
4.5%
2023 208
44.4%
2022 20
 
4.3%
2021 198
42.3%
2020 7
 
1.5%
2019 1
 
0.2%
2018 3
 
0.6%
2017 2
 
0.4%

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)2.6%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1.4689507
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-03-26T20:16:50.803935image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5.7
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7568381
Coefficient of variation (CV)1.1959816
Kurtosis15.622017
Mean1.4689507
Median Absolute Deviation (MAD)0
Skewness4.0259179
Sum686
Variance3.0864802
MonotonicityNot monotonic
2025-03-26T20:16:50.921474image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 425
90.8%
2 8
 
1.7%
9 7
 
1.5%
8 7
 
1.5%
4 6
 
1.3%
3 3
 
0.6%
7 3
 
0.6%
11 3
 
0.6%
6 2
 
0.4%
5 1
 
0.2%
Other values (2) 2
 
0.4%
ValueCountFrequency (%)
1 425
90.8%
2 8
 
1.7%
3 3
 
0.6%
4 6
 
1.3%
5 1
 
0.2%
6 2
 
0.4%
7 3
 
0.6%
8 7
 
1.5%
9 7
 
1.5%
10 1
 
0.2%
ValueCountFrequency (%)
12 1
 
0.2%
11 3
0.6%
10 1
 
0.2%
9 7
1.5%
8 7
1.5%
7 3
0.6%
6 2
 
0.4%
5 1
 
0.2%
4 6
1.3%
3 3
0.6%

quarter
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.9%
Missing1
Missing (%)0.2%
Memory size27.5 KiB
1.0
436 
3.0
 
17
2.0
 
9
4.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1401
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 436
93.2%
3.0 17
 
3.6%
2.0 9
 
1.9%
4.0 5
 
1.1%
(Missing) 1
 
0.2%

Length

2025-03-26T20:16:51.044613image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T20:16:51.145575image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 436
93.4%
3.0 17
 
3.6%
2.0 9
 
1.9%
4.0 5
 
1.1%

Most occurring characters

ValueCountFrequency (%)
. 467
33.3%
0 467
33.3%
1 436
31.1%
3 17
 
1.2%
2 9
 
0.6%
4 5
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1401
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 467
33.3%
0 467
33.3%
1 436
31.1%
3 17
 
1.2%
2 9
 
0.6%
4 5
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1401
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 467
33.3%
0 467
33.3%
1 436
31.1%
3 17
 
1.2%
2 9
 
0.6%
4 5
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1401
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 467
33.3%
0 467
33.3%
1 436
31.1%
3 17
 
1.2%
2 9
 
0.6%
4 5
 
0.4%

season
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.9%
Missing1
Missing (%)0.2%
Memory size28.9 KiB
Winter
434 
Summer
 
12
Fall
 
11
Spring
 
10

Length

Max length6
Median length6
Mean length5.9528908
Min length4

Characters and Unicode

Total characters2780
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter
2nd rowWinter
3rd rowWinter
4th rowWinter
5th rowWinter

Common Values

ValueCountFrequency (%)
Winter 434
92.7%
Summer 12
 
2.6%
Fall 11
 
2.4%
Spring 10
 
2.1%
(Missing) 1
 
0.2%

Length

2025-03-26T20:16:51.278351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T20:16:51.394118image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
winter 434
92.9%
summer 12
 
2.6%
fall 11
 
2.4%
spring 10
 
2.1%

Most occurring characters

ValueCountFrequency (%)
r 456
16.4%
e 446
16.0%
n 444
16.0%
i 444
16.0%
W 434
15.6%
t 434
15.6%
m 24
 
0.9%
S 22
 
0.8%
l 22
 
0.8%
u 12
 
0.4%
Other values (4) 42
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 456
16.4%
e 446
16.0%
n 444
16.0%
i 444
16.0%
W 434
15.6%
t 434
15.6%
m 24
 
0.9%
S 22
 
0.8%
l 22
 
0.8%
u 12
 
0.4%
Other values (4) 42
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 456
16.4%
e 446
16.0%
n 444
16.0%
i 444
16.0%
W 434
15.6%
t 434
15.6%
m 24
 
0.9%
S 22
 
0.8%
l 22
 
0.8%
u 12
 
0.4%
Other values (4) 42
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 456
16.4%
e 446
16.0%
n 444
16.0%
i 444
16.0%
W 434
15.6%
t 434
15.6%
m 24
 
0.9%
S 22
 
0.8%
l 22
 
0.8%
u 12
 
0.4%
Other values (4) 42
 
1.5%

team_size
Real number (ℝ)

Missing 

Distinct17
Distinct (%)3.8%
Missing23
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean5.4808989
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-03-26T20:16:51.492214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile10
Maximum17
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9027354
Coefficient of variation (CV)0.52960938
Kurtosis1.0886609
Mean5.4808989
Median Absolute Deviation (MAD)2
Skewness0.79859303
Sum2439
Variance8.4258731
MonotonicityNot monotonic
2025-03-26T20:16:51.618261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4 65
13.9%
6 57
12.2%
5 54
11.5%
7 50
10.7%
2 49
10.5%
3 44
9.4%
8 41
8.8%
9 28
6.0%
1 25
 
5.3%
10 12
 
2.6%
Other values (7) 20
 
4.3%
(Missing) 23
 
4.9%
ValueCountFrequency (%)
1 25
 
5.3%
2 49
10.5%
3 44
9.4%
4 65
13.9%
5 54
11.5%
6 57
12.2%
7 50
10.7%
8 41
8.8%
9 28
6.0%
10 12
 
2.6%
ValueCountFrequency (%)
17 2
 
0.4%
16 1
 
0.2%
15 1
 
0.2%
14 4
 
0.9%
13 3
 
0.6%
12 3
 
0.6%
11 6
 
1.3%
10 12
 
2.6%
9 28
6.0%
8 41
8.8%

unique_roles
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)1.3%
Missing23
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean1.6876404
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-03-26T20:16:51.745042image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0983013
Coefficient of variation (CV)0.65079107
Kurtosis1.2146035
Mean1.6876404
Median Absolute Deviation (MAD)0
Skewness1.4625409
Sum751
Variance1.2062658
MonotonicityNot monotonic
2025-03-26T20:16:51.858522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 294
62.8%
3 62
 
13.2%
2 48
 
10.3%
4 32
 
6.8%
5 7
 
1.5%
6 2
 
0.4%
(Missing) 23
 
4.9%
ValueCountFrequency (%)
1 294
62.8%
2 48
 
10.3%
3 62
 
13.2%
4 32
 
6.8%
5 7
 
1.5%
6 2
 
0.4%
ValueCountFrequency (%)
6 2
 
0.4%
5 7
 
1.5%
4 32
 
6.8%
3 62
 
13.2%
2 48
 
10.3%
1 294
62.8%

Interactions

2025-03-26T20:16:43.943145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:41.368631image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:41.974181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:42.731338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:43.293889image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:44.062974image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:41.503623image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:42.088881image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:42.852162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:43.402998image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:44.185566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:41.653113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:42.240808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:42.987814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:43.550480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:44.310246image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:41.761006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:42.364819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:43.090731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:43.667442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:44.430173image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:41.871158image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:42.620558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:43.196957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-26T20:16:43.777826image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-03-26T20:16:51.941530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
episode_countyearmonthquarterteam_sizeunique_roles
0
episode_count1.000-0.053-0.008-0.008-0.029-0.109
year-0.0531.000-0.007-0.029-0.006-0.121
month-0.008-0.0071.0000.986-0.262-0.144
quarter-0.008-0.0290.9861.000-0.238-0.131
team_size-0.029-0.006-0.262-0.2381.0000.217
unique_roles-0.109-0.121-0.144-0.1310.2171.000
2025-03-26T20:16:52.067859image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
episode_countyearmonthquarterteam_sizeunique_roles
0
episode_count1.000-0.0450.0180.034-0.007-0.125
year-0.0451.0000.1450.060-0.032-0.158
month0.0180.1451.0000.869-0.368-0.186
quarter0.0340.0600.8691.000-0.296-0.150
team_size-0.007-0.032-0.368-0.2961.0000.159
unique_roles-0.125-0.158-0.186-0.1500.1591.000
2025-03-26T20:16:52.197462image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
episode_countyearmonthquarterteam_sizeunique_roles
0
episode_count1.000-0.0410.0170.033-0.006-0.115
year-0.0411.0000.1350.056-0.025-0.142
month0.0170.1351.0000.861-0.307-0.173
quarter0.0330.0560.8611.000-0.250-0.141
team_size-0.006-0.025-0.307-0.2501.0000.138
unique_roles-0.115-0.142-0.173-0.1410.1381.000
2025-03-26T20:16:52.340829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
networkdategenresubgenreepisode_countsource_typestatusorder_typeyearmonthquarterseasonteam_sizeunique_roles
0
network1.0000.9700.6090.5060.3750.5060.1240.2910.8740.7320.7290.7130.0980.526
date0.9701.0000.8720.2830.3680.3600.3060.0001.0001.0001.0001.0000.0000.000
genre0.6090.8721.0000.8770.0000.4160.0000.0000.2060.3930.4270.4830.1430.000
subgenre0.5060.2830.8771.0000.7920.7201.0000.0000.0000.0000.0000.0000.0000.000
episode_count0.3750.3680.0000.7921.0000.0000.8780.2630.1900.1770.2840.2560.0000.062
source_type0.5060.3600.4160.7200.0001.0000.2330.0000.0000.0000.0430.0600.3080.053
status0.1240.3060.0001.0000.8780.2331.0000.1210.3550.0000.0000.0000.0000.120
order_type0.2910.0000.0000.0000.2630.0000.1211.0000.0000.0000.0000.0000.1290.566
year0.8741.0000.2060.0000.1900.0000.3550.0001.0000.7240.7080.5880.0000.000
month0.7321.0000.3930.0000.1770.0000.0000.0000.7241.0001.0000.9990.0000.000
quarter0.7291.0000.4270.0000.2840.0430.0000.0000.7081.0001.0000.9600.2730.000
season0.7131.0000.4830.0000.2560.0600.0000.0000.5880.9990.9601.0000.2360.000
team_size0.0980.0000.1430.0000.0000.3080.0000.1290.0000.0000.2730.2361.0000.440
unique_roles0.5260.0000.0000.0000.0620.0530.1200.5660.0000.0000.0000.0000.4401.000
2025-03-26T20:16:52.510852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
genrenetworkorder_typequarterseasonsource_typestatus
genre1.0000.3060.0000.2010.2310.2120.000
network0.3061.0000.2260.4880.4700.2180.096
order_type0.0000.2261.0000.0000.0000.0000.077
quarter0.2010.4880.0001.0000.7320.0250.000
season0.2310.4700.0000.7321.0000.0350.000
source_type0.2120.2180.0000.0250.0351.0000.177
status0.0000.0960.0770.0000.0000.1771.000
2025-03-26T20:16:52.644620image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
episode_countgenremonthnetworkorder_typequarterseasonsource_typestatusteam_sizeunique_rolesyear
episode_count1.0000.0000.0180.1650.1960.1290.1160.0000.696-0.007-0.125-0.045
genre0.0001.0000.1990.3060.0000.2010.2310.2120.0000.0690.0000.102
month0.0180.1991.0000.3830.0000.9930.9820.0000.000-0.368-0.1860.145
network0.1650.3060.3831.0000.2260.4880.4700.2180.0960.0210.2760.495
order_type0.1960.0000.0000.2261.0000.0000.0000.0000.0770.0990.4080.000
quarter0.1290.2010.9930.4880.0001.0000.7320.0250.0000.1650.0000.540
season0.1160.2310.9820.4700.0000.7321.0000.0350.0000.1420.0000.417
source_type0.0000.2120.0000.2180.0000.0250.0351.0000.1770.0950.0270.000
status0.6960.0000.0000.0960.0770.0000.0000.1771.0000.0000.0860.352
team_size-0.0070.069-0.3680.0210.0990.1650.1420.0950.0001.0000.159-0.032
unique_roles-0.1250.000-0.1860.2760.4080.0000.0000.0270.0860.1591.000-0.158
year-0.0450.1020.1450.4950.0000.5400.4170.0000.352-0.032-0.1581.000

Missing values

2025-03-26T20:16:44.600668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-26T20:16:44.925901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-26T20:16:45.161936image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

show_namekey_creativesnetworkstudiodategenresubgenreepisode_countsource_typestatusorder_typenotesyearmonthquarterseasonteam_sizeunique_roles
06666Taylor Sheridan (ep), John Linson (ep), Art Linson (ep), David Glasser (ep), Ron Burkle (ep), Bob Yari (ep)Paramount+101 Studios, MTV Entertainment Studios2023-01-01Drama0OriginalSet when Comanches still ruled West Texas. No ranch in America is more steeped in the history of the West than the 6666. Encompassing an entire county, it is where the rule of law and the laws of nature merge in a place where the most dangerous thing one does is the next thing. The 6666 is synonymous with the merciless endeavor to raise the finest horses and livestock in the world and ultimately where world-class cowboys are born and made.2023.01.01.0Winter6.01.0
13 Body ProblemAlexander Woo, David Benioff, D.B. Weiss, Derek Tsang, Rian Johnson, Ram Bergman, Brad Pitt, Jeremy Kleiner, Dede Gardner, Rosamund Pike, Robie Uniacke, Lin Qi, Bernadette Caulfield, Nena RodrigueNetflixPlan B2021-01-01Sci-FiProcedural0Book2021.01.01.0Winter14.01.0
261st StreetPeter Moffat, J. David Shanks, Michael B. Jordan, Alana Mayo, Hilary SalmonAMCAMC Studios2023-01-01DramaTrue Crime, Legal0Original2023.01.01.0Winter5.01.0
39-1-1: NashvilleRashad Raisani, Ryan Murphy, Tim MinearABC2025-02-20DramaSuperhero, Procedural0TV Show2025.02.01.0Winter3.01.0
4A Knight of the Seven Kingdoms: The Hedge KnightGeorge R.R. Martin, Ira Parker, Ryan Condal, Vince GerardisHBO2023-01-01DramaYoung Adult0Book2023.01.01.0Winter4.01.0
5A League of Their OwnAbbi Jacobson, Will GrahamPrime VideoSony Pictures Television2021-01-01Drama0Film2021.01.01.0Winter2.01.0
6A Man in FullDavid E. Kelley (Writer, Showrunner, Executive Producer), Jeff Daniels (Actor), Diane Lane (Actor), William Jackson Harper (Actor), Regina King (Director, Executive Producer), Matthew Tinker (Executive Producer)NetflixNetflix Studios2023-01-01Drama6BookLimitedCenters on Charlie Croker, a polarizing and robust Atlanta real estate mogul who faces sudden bankruptcy. Crude, rude and irrepressible, he defends his empire against all takers. At any cost.2023.01.01.0Winter6.04.0
7A Small LightJoan Rater (w, ep), Tony Phelan (w, ep), Susanna Fogel (d, ep), Peter Traugott (ep), Alon Shtruzman (ep), Avi Nir (ep)Paramount+ABC Signature, Keshet Studios2023-01-01Drama0OriginalWhen twentysomething Miep Gies' boss Otto Frank came to her and asked her to hide his family from the Nazis during World War II, she didn’t hesitate. For the next two years, Miep, her husband and the other helpers watched over the eight souls in hiding in the Secret Annex. And it was Miep who found Anne Frank’s Diary and kept it safe so Otto, the only one of the eight who survived, could later share it with the world.2023.01.01.0Winter6.03.0
8AcapulcoAustin Winsberg, Chris Harris, Eduardo Cisneros, Jason Shuman, Eugenio Derbez, Benjamin Odell, Richard ShepardApple TV+Lionsgate Television2023-01-01Comedy0Film2023.01.01.0Winter7.01.0
9Agatha: Coven of ChaosJac Schaeffer (w, ep)Paramount+Marvel Television2023-01-01Drama0OriginalWandaVision' spinoff focusing on Agatha Harkness character. Plot details under wraps.2023.01.01.0Winter1.01.0
show_namekey_creativesnetworkstudiodategenresubgenreepisode_countsource_typestatusorder_typenotesyearmonthquarterseasonteam_sizeunique_roles
458Winning Time: The Rise of the Lakers DynastyMax Borenstein, Adam McKay, Kevin Messick, Jim Hecht, Jason Shuman, Scott Stephens, Rodney BarnesHBOHyperobject Industries2021-01-01DramaSports0Original2021.01.01.0Winter7.01.0
459Witch MountainTravis Fickett (w, ep), Terry Matalas (w, ep), Augustine Frizzell (d, ep), John Fox (ep), John Davis (ep), Gary Marsh (ep)Paramount+ABC Signature2023-01-01Drama0OriginalModern reinvention of movie franchise takes place in the shadow of Witch Mountain, following two teens who develop strange abilities and discover their sleepy suburb might not be as idyllic as it seems.2023.01.01.0Winter6.03.0
460With LoveGloria Calderón Kellett, Meera MenonPrime VideoAmazon Studios2021-01-01Drama0Original2021.01.01.0Winter2.01.0
461Women of the MovementMarissa Jo Cerar (w, ep, sr), Jay-Z (ep), Jay Brown (ep), Tyran “Ty Ty” Smith (ep), Will Smith (ep), James Lassiter (ep), Aaron Kaplan (ep), Dana Honor (ep), Michael Lohmann (ep), Gina Prince-Bythewood (d, ep), Rosanna Grace (ep), Alex Foster (ep), John Powers Middleton (ep), David Clark (ep), Tina Mabry (d), Julie Dash (d), Kasi Lemmons (d)ABCKapital Entertainment2021-01-01Drama0Original2021.01.01.0Winter17.04.0
462Wonder ManDestin Daniel Cretton (d, ep), Andrew Guest (w, ep), Stella Meghie (d)Paramount+Marvel Television2023-01-01Drama0ComicFollows Simons Williams, who in the comics is a scion of wealthy industrialist who his company losing out to Tony Stark’s Stark Industries. Williams goes to work for the villain Baron Zemo, who gives him ionic talents including extreme strength. Once an adversary of the Avengers, Wonder Man ultimately teams with them.2023.01.01.0Winter3.03.0
463WoolGraham Yost, Morten Tyldum, Rebecca Ferguson, Hugh Howey, Remi Aubuchon, Nina Jack, Ingrid EscajedaApple TV+AMC Studios2023-01-01DramaScience Fiction0Book2023.01.01.0Winter7.01.0
464WytchesScott Snyder, Jock, Kevin KoldePrime VideoAmazon Studios2023-01-01Drama0Comic2023.01.01.0Winter3.01.0
465Young Sheldon SpinoffChuck Lorre, Steve Holland, Steven Molaro, Montana Jordan, Emily OsmentCBS2024-01-12Comedy0TV Show2024.01.01.0Winter5.01.0
466Zero DayEric Newman, Noah Oppenheim, Michael S. Schmidt, Jonathan Glickman, Lesli Linka Glatter, Robert De NiroNetflix2023-01-01Drama0Original2023.01.01.0Winter6.01.0
467Test 345john test (Network Executive, Director), jane test (Host, Showrunner)AMCAnonymous Content2020-01-01ComedyBroadway7FilmCancelledLimitedtest2020.01.01.0Winter2.02.0